Inter-layer adhesion in material extrusion 3D printing: effect of processing and molecular variables

There has been extensive research in the field of material-extrusion (Mat-Ex) 3D printing to improve the inter-layer bonding process. Much research focusses on how various printing conditions may be detrimental to weld strength; many different feedstocks have been investigated along with various additives to improve strength. Surprisingly, there has been little attention on how fundamental molecular properties of the feedstock, in particular the average molar mass of the polymer, may contribute to microstructure of the weld. Here we show that weld strength increases with decreasing average molar mass, contrary to common observations in specimens processed in more traditional ways, e.g., by compression molding. Using a combination of synchrotron infra-red polarization modulation microspectroscopy measurements and continuum modelling, we demonstrate how residual molecular anisotropy in the weld region leads to poor strength and how it can be eradicated by decreasing the relaxation time of the polymer. This is achieved more effectively by reducing the molar mass than by the usual approach of attempting to govern the temperature in this hard to control non-isothermal process. Thus, we propose that molar mass of the polymer feedstock should be considered as a key control parameter for achieving high weld strength in Mat-Ex

Aligned and Non-Aligned Double JPEG Detection Using Convolutional Neural Networks

Due to the wide diffusion of JPEG coding standard, the image forensic community has devoted significant attention to the development of double JPEG (DJPEG) compression detectors through the years. The ability of detecting whether an image has been compressed twice provides paramount information toward image authenticity assessment. Given the trend recently gained by convolutional neural networks (CNN) in many computer vision tasks, in this paper we propose to use CNNs for aligned and non-aligned double JPEG compression detection. In particular, we explore the capability of CNNs to capture DJPEG artifacts directly from images. Results show that the proposed CNN-based detectors achieve good performance even with small size images (i.e., 64x64), outperforming state-of-the-art solutions, especially in the non-aligned case. Besides, good results are also achieved in the commonly-recognized challenging case in which the first quality factor is larger than the second one.Comment: Submitted to Journal of Visual Communication and Image Representation (first submission: March 20, 2017; second submission: August 2, 2017

Dynamics of Self-Propelled Particles: Diffusion, Motility-Sorting, and Rectification

Self-propelled particles, or active particles, continuously convert stored energy into kinetic energy, and are therefore intrinsically out of thermodynamic equilibrium. Self-propelled particles have very different behaviors than their passive counterparts, and show very rich collective phenomena. In the last few years, the number of investigations on active particles has significantly grown, but a general picture connecting the emergence of similar collective behaviors from a great variety of systems is still lacking. Here, the dynamics of self-propelled rod-like particles in two dimensions is investigated by means of numerical simulations. The main model we use corresponds to Run-and-Tumble particles which move straight for certain time (run), until they randomly change direction of motion (tumble). The sequence of these runs and tumbles leads to a kind of random walk that nicely models the motion of flagellated bacteria like {\it E. coli}. We first study the diffusive motion of self-propelled elongated particles in the bulk. In a particular region of the particle length-velocity space, the rotational diffusion coefficient increases with density. This is in strong contrast to the case of passive elongated Brownian particles, where the presence of neighboring particles always diminish each particle's rotational motion. This enhancement of the rotation due to the particle activity can be understood with a simple active-gas picture. In this active-gas approximation, collision events are treated as two-particle point-like collisions, where no mutual alignment is induced. Increasing the particle aspect ratio, collisions among particles induce particle alignment, such that after each collision particles move together for some time, eventually forming larger clusters. The active-gas picture is no longer valid and rotational diffusion decreases with density. Spontaneous segregation of active particles with different velocities in microchannels is also investigated. Self-propelled particles are known to accumulate in the proximity of walls. Here we show how fast particles expel slower ones from channel walls, leading to a segregated state. The mechanism is characterized as a function of particle velocities, particle density, and channel width. In the presence of capillary flow, self-propelled particles show upstream swimming at the channel walls. Since this effect depends on particle motility, we show that the solvent velocity can be tuned to segregate slow and fast particles. Promising applications can be found in the development of microfluidic lab-on-a-chip devices for sorting of particles with different motilities. Finally, the motion of self-propelled particles in microchannels with asymmetric ratchet-like walls is analyzed. The asymmetry of the channel induces a net flux of particles in a determined direction with a flow which shows to be planar. We quantify the average flow velocity as a function of the relevant parameters of the self-propelled particles and the microchannel geometry. The results can be explained in terms of single-particle trajectories in the non-tumbling limit. With increasing particle density, the ratchet effect strongly decreases. Only in some cases, when particles get trapped in acute angles, a semi-dilute system performs better than a dilute one. For two-component systems, the separation of fast and slow particles is approximately proportional to the ratchet effect of single-component systems. Although the channel with ratchet-like walls does not need any imposed flow to separate fast and slow particles along the channel main axis, it turns out to be less effective in separating fast and slow particles compared to a channel with Poiseuille flow. The results presented here are quite general since they are not dependent on the specific details of the self-propelled mechanism. Sample results obtained with run-and-tumble particles have been compared with results obtained with other models. The results we present are of great theoretical and practical interest, and the give new insights into the fascinating world of self-propelled particles and off-equilibrium systems. The presented findings are of particular relevance in the design of microfluidic lab-on-chip devices, where the manipulation, the transport, the control, and the directed motion of particles is achieved without the use of laser fields or other external invasive force fields

evaluation of the spring in of cfrp thin laminates in dependence on process variation

Abstract The cure process of CFRP laminates induces residual stress inside the parts that causes geometrical unconformities. The most important unconformity is the spring-in that means the deviation of the flange-to-flange angle from the design angle. The spring-in value depends on some process parameters, such as the lay-up sequence of the plies, as demonstrated in previous works. The aim of this work is to study the dependence of the spring-in on the deviations in the orientation of the plies due to a hand process. A numerical tool was developed and experimentally tested